As engineering biology enters the age of AI, the quality, traceability, and integrity of scientific data have become critical competitive advantages. This white paper explores why fragmented laboratory records, disconnected datasets, and poor provenance tracking remain major barriers to reproducibility, technology transfer, regulatory compliance, and AI-driven discovery. It argues that version control—long established in software development—is the missing layer in biological R&D.
Through real-world case studies spanning strain engineering, CRISPR workflows, and biosecurity, the paper demonstrates how connecting physical biological assets to structured, version-controlled digital records can transform research efficiency, strengthen IP and regulatory confidence, and unlock the full value of AI-ready datasets.
Read the full white paper to discover why data infrastructure may be the most important investment in the future of engineering biology. LINK